Insurance product, rating and credit enhancement system and method for insuring project savings

ABSTRACT

In the present invention, an insurance product, rating system and method generally relates to a rating and pricing system for quantifying the risk that the annual savings will not fall below specified levels associated with implementing and maintaining economic improvements. Disclosed embodiments involve a credit enhancement system and method relating to the manner in which insuring a project savings floor (i.e., a minimum level of return on investment for a given improvement project) can be realized in a better credit rating for bonds or loans associated with the improvement project. The product, system and method can be applied to various industries, including, power generation, petro-chemical, manufacturing and refining facilities. Various embodiments disclosed herein relate to a system and method for establishing a rating system to determine the impact on an insured&#39;s credit risk.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of prior U.S. patent application Ser. No. 13/458,598, entitled “INSURANCE PRODUCT, RATING SYSTEM AND METHOD,” filed on Apr. 24, 2012, which is in turn a divisional patent application of prior U.S. patent application Ser. No. 11/153,305, entitled “INSURANCE PRODUCT, RATING SYSTEM AND METHOD,” filed on Jun. 15, 2005, both of which are incorporated herein in their entirety.

STATEMENTS REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

N/A

REFERENCE TO A MICROFICHE APPENDIX

N/A

BACKGROUND OF THE INVENTION Field of the Invention

Pricing and rating methods for property and property-related asset performance insurance products can be classified into two categories: Value-based (VB) rating and Frequency-Severity (FS) rating. In both cases insurance costs are directly related to the financial loss potentials, but the computational methods reflect the characteristics of the property or assets being insured.

VB rating generally is applied to situations where risk or loss potential can be characterized by a series of variables. For example, the loss potential and pricing for a new car may be determined by the car type, the type of loss (e.g., collision, liability, glass windshield) the amount and type of miles driven, the driving record of the insured, the geographical location and perhaps other variables. Given these variables, loss potentials have been analyzed and tables produced enabling the underwriter to look up the rates, expressed in dollars of premium/dollar of coverage, in tables. The underwriter typically multiplies the client-specific variables by the corresponding rates then adds in company-specific administrative costs to compute the overall policy premium.

For property VB insurance, some common underwriting variables are business type, building activity (e.g., hospitals, office buildings, laboratories, etc.), square footage or other attributes of size, construction attributes, fire sprinkler coverage, number of stories, location, and age. Premium rates expressed are generally categorized by these variables and together produce a premium rate. This value multiplied by the building value produces the policy premium. Actual premium values may vary by historical precedent of pricing, market demands, policy terms and conditions, contents type and property replacement values.

FS pricing is a rating and pricing method for situations where there can be large differences between insureds in the same type of industry and geographical area. In this method the probability or failure frequency (events/year) of an insurance claim or failure may be modeled or directly obtained from available data.

Engineering and underwriting risk modifiers are factors applied to the loss cost computed premium that adjust for specific customer attributes present in the current situation. For example, an engineering risk modification factor to increase the loss cost 10% could be applied for clients who have poor procedures for record-keeping and plant cleanliness. Engineering inspectors have identified a high correlation with these behaviors and customers who will have insurance claims. An underwriting risk modification factor of 10% could decrease the policy premium if high deductibles and restricted coverages are negotiated with the client. These engineering and underwriting risk modification factors make detailed premium changes based on the specific attributes of the client and the policy terms and conditions.

An example of the FS pricing method for a client is applied to an equipment breakdown premium development for a power generation station 100 shown in FIG. 1. The station has two (2) simple cycle GE 7FA turbine generators 102, 104 with two (2) transformers 106, 108 and various types of electrical switchgear and equipment (only switch 110 is shown). The first part of the premium calculation contains the frequency and severity calculation which determines the loss cost component of the premium. There are risk modification factors that customize the loss cost component for the specific client being analyzed. These factors can increase or decrease the credit and debit percentage that allows underwriting to modify the loss cost to reflect the subjective attributes (e.g., engineering factors) of the client, for example, housekeeping, recordkeeping, reliability planning, the number of equipment spares available and underwriting factors such as the deductible value selected.

The next part of the premium calculation determines the client-specific expenses, costs and profit. Another component of the premium calculation, the Excess Loss Potential refers to a loss cost premium component that accounts for the very low frequency, but very high severity loss events that are appropriate for the client. Examples of such loss events include five hundred (500) year recurrence period earthquakes, tsunamis and hurricanes. The loss event severities may be determined by specialized catastrophic modeling software. A portion of the insurance company's total loss potential may be allocated to each client as the Excess Loss Potential component of the premium.

The client may also be subjected to engineering inspections associated with jurisdictional requirements of the state or other governmental bodies. The underwriting process also includes certain client-specific costs associated with meetings, travel and the like.

Expenses considered in the underwriting process can also include costs for re-insurance and are usually added when the underwriter buys facultative re-insurance—re-insurance on a specific account. Although other expenses that involve a pro-ration of portfolio, line of business, department, or division expenses to the account level may also be added. Other premium costs are typically taxes, commissions to brokers, profit margin and other specified premium cost adders in the company's underwriting guidelines.

The FS pricing for the example above is shown below for constructing an equipment breakdown insurance price for a simple cycle gas turbine generation facility:

Annual Failure Premium Equipment Frequency Severity (Loss Costs) 2 GE 7FA turbines 0.025 $80,000,000  $2,000,000 2 Transformers 0.015 $4,000,000 $60,000 Switchgear + Electrical 0.030 $1,000,000 $30,000 Total Loss Costs: $2,090,000 Engineering/Underwriting Modifier (+20% − 15%) [−10%] $1,881,000 Excess Loss Potential: $100,000 Engineering Expenses $25,000 Underwriting Expenses $10,000 Allocated Expenses $300,000 Taxes, Commissions $30,000 Profit (5%) $115,000 Total Policy Premium: $2,461,000

Policy rating and pricing applied to property-related insurance pricing generally is a combination of applying the VB and FS methods. The insured's (client) property often contains a mix of highly specific equipment and other activities that are common to many similar types of locations. A client's power generation company may own a small number of highly specialized power generation locations that are rated and priced using FS but also has several branch offices where the premium may be computed by the VB method.

Moreover, improvement projects come in various forms. Some improvement projects involve new construction activities designed to have a sufficient return that pays for the capital costs and on-going expenses such as pipelines, toll roads, or bridges. Others improvement project can involve retro-fit alterations of existing structures to increase throughput, decrease energy costs, decrease operating costs, comply with emission regulations, improve reliability, increase safety, or other activities that benefit the organization.

The design and implementation of these activities usually involve application of capital in some form. Some companies fund these projects from internal resources. However, many projects seek external funding where banks, investors, and other financial institutions are asked to lend money for projects that are planned to produce.

Financial underwriters closely review the credit attributes of the project developers and many aspects of their financial history. The project specifics are also reviewed for the internal rate of return, return on investment, and other performance metrics that depend on the experience and technology understanding of the lenders.

Generally, lenders require projects to cover standard insurance risks that are widely available in the marketplace, such as, for example, builder's risk, property all risk, general liability and others. These risk transfer instruments help reduce the lender's risk for loan repayment and are considered basic attributes of many project finance transactions. As a result, while they effectively reduce project risk and thereby credit risk, these insurances are taken as required prerequisites for finance transactions and as such are not considered in credit rating analyses.

The various embodiments disclosed herein are generally intertwined with the embodiments disclosed in U.S. Pat. No. 8,195,484, entitled “Insurance Product, Rating System and Method, which is incorporated herein in its entirety, and relates to a rating and pricing system for quantifying and insuring the risk that the annual savings will not fall below specified levels associated with implementing and maintaining economic improvements. Specifically, the various embodiment disclosed herein relate to the credit enhancement aspect and functionality of including the insured's savings stream in the loan repayment schedule.

BRIEF SUMMARY OF THE INVENTION

The embodiments disclosed herein involve a credit enhancement system and method and generally relate to the manner in which insuring a project savings floor (i.e., a minimum level of return on investment for a given improvement project) can be realized in a better credit rating for bonds or loans associated with the improvement project. The project savings floor or a minimum level of results can refer to separate and distinct annual returns for a multi-year policy or an aggregate return over a multi-year policy term.

As used herein, “savings” can be tangible or intangible and include but are not limited to increased revenue; reduced operational expenses maintenance expenses and capital expenditures; increased production through-put; reduced energy consumption; reduced emissions; increased emission credits; etc. These savings will produce additional benefits to the client in the form of enhanced creditworthiness, which results in an increase in the availability of financing as well as a reduced cost of financing. One skilled in the art will recognize that the scope of the various embodiments disclosed herein includes other savings and benefits not specifically articulated in the lists above.

The disclosed embodiments are not limited to signifying a higher rating for a particular project's loan caused only by the effective reduction in re-payment stress due to including the project savings in the credit risk calculation. Rather, the disclosed embodiments also aim to quantify the credit enhancement just from the inclusion of project savings and the insured floor. Other factors external to these may also influence the rating process and these need to be considered by those skilled in the specific rating methodology being applied to the project under consideration.

Insurance pricing systems where there may be a large amount of exposure and loss data available use standard statistical and probabilistic methods. Policies are often standardized in format and simplified to the point where underwriters construct premiums from tables where the risk attributes such as insured's age, car type, location, or building values are the key elements used to lookup the appropriate rates. Other insurance policies, such as property insurance policies, may include a premium component developed from catastrophe models which estimate losses from acts of nature, such as earthquakes, for example.

Insurance pricing systems are typically designed for products which are marketed to a large number of customers usually on an annual basis, each with a relatively small loss potential. Conversely, the disclosed embodiments typically comprise an insurance product rating and pricing system designed for a relatively small number of insureds annually or over a multi-year term with each insured having a relatively large exposure. This latter situation cannot rely on the Law of Large Numbers principle of statistics but applies as much knowledge and actual performance data as possible into the development of the risk analysis and subsequently the premium development.

The insurance policy rating and pricing systems and method according to some embodiments disclosed herein are based on a risk analysis where actual performance data, technical uncertainties, and other factors are combined to form input information for the pricing system. The input files, called annual aggregate risk distributions, quantify the net performance risk of all the initiatives for achieving the net annual savings for each year of the policy period. For example, an improvement program may consist of work force reassignments, process re-designs, installation of advanced process controls, and energy efficiency capital projects. However, the various embodiments disclosed herein are not so limited. As a further example, it also applies to other methods capable of quantifying the total net annual savings risk and credit improvement attributes of potentially several hundred initiatives. These risk distributions quantify the probability of exceeding a given net annual savings value and serve as the fundamental input files, data, or equations according to the disclosed embodiments. Accordingly, the embodiment disclosed herein facilitate a risk-based decision-making system and method to help evaluate project savings risk, credit risk improvements from project savings, and the value created from insuring a minimum level of savings.

The aggregate risk distributions are defined for each location on a similar basis as those applied in developing property insurance. Underwriting may be first performed at a location level and then viewed at the client level annually or in aggregate over the project term. At least one novel aspect of some embodiments relates to enabling the underwriter to develop pricing at various levels and time period designations. At the location level, the aggregate risk distributions are formed for the subset of all initiatives designed to be implemented at the location. At the client level, the aggregation produces only one aggregate risk distribution per year or other time periods.

While the disclosed embodiments are generally discussed from the perspective of either pricing a single location or pricing at a single client level, a multi-client pricing system is also within the scope of the disclosed embodiments. “Multi-client,” as used herein, includes but is not limited to an investor(s) in one or more facilities, for example power, refining, chemical, manufacturing facilities, etc. in any permutation or combination of ownership and/or geography.

One embodiment is a computer implemented method for assessing a project loan credit rating enhancement associated with implementing an improvement plan for a facility, embodied in computer executable instructions stored in a non-transitory computer readable medium, that when executed, cause a computer system having a memory and a processor specifically programmed by the computer executable instruction to perform a method of pricing an insurance policy, the method comprising: receiving a loan interest rate and default data, receiving a user specified loan amount, a loan term, and a payment schedule, receiving project savings distribution data, receiving a required project savings value for an insured, determining equivalent credit risk relationships, computing the probability of achieving the required project savings to realize the credit rating equivalence, and computing credit risk savings from possible credit improvement possibilities.

Another embodiment is a computerized system for assessing a project loan credit rating enhancement associated with implementing an improvement plan for a facility comprising: display means for prompting a user to enter a loan interest rate, default data, a loan amount, a loan term, and a payment schedule, storage means for receiving and storing project savings distribution data and a minimum level of savings for an insured, and a processor specifically programmed to: determine equivalent credit risk relationships, compute the probability of achieving the required project savings to realize the credit rating equivalence, and compute credit risk savings from possible credit improvement possibilities.

Other embodiments are a method for utilizing a computer system for assessing credit enhancement for an insured associated with implementing an improvement plan by the insured, wherein the computer system comprises an input unit, a processor, a storage unit, and an output unit, the method comprising: developing a project improvement plan; compiling project improvement distribution data, receiving input data from the input unit, the input data comprising loan interest rates, rating categories corresponding to the loan interest rates, default probabilities, a loan amount, and term and payment schedule, utilizing the storage unit to store the project improvement plan data and the input data. These embodiments further comprise utilizing the processor to: compute an insured floor of savings, compute a project savings distribution based on the insured floor of savings and the project improvement distribution data, compute a net periodic loan payment based on the project savings distribution and the input data, compute default amounts of each rating category at each fractional level of applied project savings, and compute average default loss values for each fractional level of applied project savings for the periodic payment for each rating category. Some embodiments further comprise utilizing the storage unit to store the average default loss values, and utilizing the output unit to display the computed data.

Still other embodiments relate to a method for running a computer system for calculating possible equivalent risk relationships, the computer system comprising: an input unit for inputting data into the system, a processor for computing the inputted data, an output unit for displaying the computed data, a storage system for storing the computed data, the method comprising the steps of: inputting data by means of the input unit, the inputted data comprising loan interest rate and default probabilities, user specified loan amount, term and payment schedule, and a project savings distribution, computing net periodic loan payment incorporating the different confidences of the project savings from the inputted data by means of the processor, computing default amounts of each rating category at each fractional level of applied project savings by means of the processor, computing average default loss values for each fractional level of applied project savings for the periodic payment for each rating category by means of the processor, saving the average default loss values by means of the storage device, and displaying the computed data by means of the output unit.

Other embodiments relates to a computer system comprising an input unit for inputting data into the system, a processor for computing the inputted data, an output unit for displaying the computed data, a storage system and a computer program stored by the storage system comprising instructions that, when executed, cause the processor to compute net periodic loan payment incorporating the different confidences of the project savings from the inputted data, compute default amounts of each rating category at each fractional level of applied project savings, and compute average default loss values for each fractional level of applied project savings for the periodic payment for each rating category.

Further embodiments relate to a computer program for calculating possible equivalent risk relationships, the computer program stored by a storage system of a computer system comprising an input unit for inputting data into the system, a processor for computing the inputted data, an output unit for displaying the computed data, the computer program comprising instructions that, when executed, cause the processor to compute net periodic loan payment incorporating the different confidences of the project savings from the inputted data, compute default amounts of each rating category at each fractional level of applied project savings, and compute average default loss values for each fractional level of applied project savings for the periodic payment for each rating category.

The disclosed system and methods provide a fast and reliable way to provide data on a project saving floor or a minimum level of savings that can be realized in form of a better credit rating for bonds or loans associated with the improvement project. The advantages obtained by the various disclosed computer system embodiments are the same as the advantages described in relation to the disclosed method embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

A better understanding of the present invention can be obtained when the following detailed description of the preferred embodiment is considered in conjunction with the following drawings, in which:

FIG. 1 is a block diagram of a power generation station.

FIG. 2 is a flowchart of an embodiment of the claimed product, system and method.

FIG. 3 is a flowchart of an embodiment of the claimed product, system and method.

FIG. 4 is a flowchart of an embodiment of the claimed product, system and method.

FIG. 5A is a flowchart of an embodiment of the claimed product, system and method.

FIG. 5B is a flowchart of an embodiment of the claimed product, system and method.

FIG. 6A is a flowchart of an embodiment of the claimed product, system and method.

FIG. 6B is a flowchart of an embodiment of the claimed product, system and method.

FIG. 7A is a spreadsheet of an embodiment of the claimed product, system and method.

FIG. 7B is a spreadsheet of an embodiment of the claimed product, system and method.

FIG. 8 is a flowchart of an embodiment of the claimed product, system and method.

FIG. 8A is a table of an embodiment of the claimed product, system and method.

FIG. 8B is a chart of an embodiment of the claimed product, system and method.

FIG. 8C is a chart of an embodiment of the claimed product, system and method.

FIG. 9 is a chart of an embodiment of the claimed product, system and method.

FIG. 10 is a system block diagram of one embodiment of the claimed product, system and method.

FIG. 11 is a block diagram of an insurance policy according to the claimed product, system and method.

FIGS. 12A-12D are tables of an embodiment of the claimed product, system and method.

FIG. 13 is a flowchart of an embodiment of the insurance product, rating and credit enhancement system and method for insuring project savings.

FIG. 14 illustrates a flowchart for determining an insured savings floor for a project.

FIG. 15 illustrates an example of how an insured savings floor is determined.

FIG. 16 illustrates an example of how insurance modifies the computed aggregate project savings risk distribution.

FIG. 17 is a flowchart illustrating the integration of the insured floor aggregate project savings risk distribution with the project credit risk calculations.

FIG. 18 illustrates an example of the graphical representation of the project information placed in tabular form.

FIG. 19 is a spreadsheet of an embodiment illustrating an example of converting default loss (i.e. credit risk) values into credit enhancement values.

FIG. 20A is a spreadsheet of an embodiment illustrating an exemplary table of probability values for exceeding the saving amount required for given credit rating enhancements.

FIG. 20B is a spreadsheet of an embodiment illustrating an exemplary table of credit risk reduction (i.e., savings) values for given credit rating enhancements.

FIG. 20C is a spreadsheet of an embodiment illustrating an exemplary table of the value of the project savings required for achieving the corresponding credit rating enhancement.

FIG. 21 is an exemplary chart for an embodiment illustrating the overall project level aggregate savings risk distribution used to determine project savings.

FIGS. 22A and 22B are exemplary spreadsheets of an embodiment illustrating credit enhancement probabilities from project savings without and with an insured floor, respectively.

FIGS. 23A and 23B are exemplary spreadsheets of an embodiment illustrating possible credit enhancement values from project savings without and with an insured floor, respectively.

FIG. 24 illustrates an embodiment of the insurance product, rating and credit enhancement system for insuring project savings.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The underwriter first determines the insured floor dollar values for each year as shown in step 200 in FIG. 2. This may be performed by specifying a confidence level that is used to return the indicated or computed minimum insured savings values for all years or confidence levels and can be applied on a year by year basis. Selecting insured floors by first specifying an explicit confidence level is one unique characteristic of this invention. For this invention, “confidence level” is defined as the probability that the annual savings will exceed the insured floor value. Performing this function is called risk acceptance. For each policy year, the underwriters select the risk acceptance level they believe represent insurable positions under the terms and conditions of the policy at step 202. The insured floors are also called risk acceptance thresholds in that if the insured's annual Savings results are below these values and the insured is in compliance with the terms and conditions of the policy, the insurer would pay the insured the difference between the actual achieved results and the insured floor value at steps 204 and 206, respectively. Under the insurance policy, the insurer is accepting the risk of paying up to the risk acceptance threshold dollar amount each year.

These risk acceptance values are also related to claim frequency as depicted in FIG. 3. The method starts at step 300 where a confidence level percentage is determined at step 302. The difference between 100 percent and the confidence level percentage constitutes the probability that the Savings may be less than the risk acceptance value at step 304. For example, a 90% confidence level indicates that 10% of the time, the Savings is expected to be less than the indicated acceptance value. While additional claim frequency mitigation elements are applied in this invention, the 100 minus confidence level may be an upper limit on the expected annual claim frequency.

Another unique characteristic of this invention is to use the confidence level approach to enable underwriters to apply different risk acceptance judgments for different policy years. This may be but one major advantage of setting deductibles by confidence level rather than directly in terms of absolute dollar values. However, underwriters can choose a risk acceptance value directly and apply the input annual aggregate risk distributions to determine the corresponding risk acceptance confidence level. Both methods are included in this invention. Also the application of input annual aggregate risk distributions to help specify multi-year deductibles is a unique part of this invention.

The flexibility of specifying yearly or overall confidence values enable underwriters to set risk acceptance values higher for years they believe there is higher risk and lower amounts when the risk is within normal tolerances. This can occur if the underwriters believe that the insured's implementation and scheduling plan will not either meet the expected Savings targets or that the project schedule is too aggressive implying that the insured's Savings will be achieved but not in the policy year indicated in the implementation and scheduling plan. This feature gives underwriters the flexibility to adapt their risk acceptance analysis to consider in addition to the insured's engineering performance, the available personnel, project management, and several other key factors.

As an example of how this process can be performed, suppose a potential insured's cumulative Savings engineering project plan forecasts $20M in year 1, $30M in year 2, and $35M in year 3 as depicted in step 400 of FIG. 4. After a detailed review of the implementation and scheduling plan by underwriting, the completion schedule for the year 1 is judged to be too optimistic. Underwriters believe that the Savings as forecast by year 1 will be obtained but some of the initiatives will extend into year 2. For the remaining initiatives, it is further concluded that the Savings targets will be achieved on the time schedule indicated in the implementation and scheduling plan for years 2 and 3.

For this situation underwriters may apply a higher confidence level for year 1 than for years 2 and 3 at step 402. A 95% confidence level could be applied to year 1 with a 90% confidence applied to years 2 and 3. The resulting risk acceptance values may be $10M for year 1, $22M for year 2, and $25M for year 3. It may be expected that the risk acceptance values will be less than the stated engineering forecasts as a matter of proper underwriting, for example, to reduce the potential for moral hazard.

With the risk acceptance values selected, the next underwriting decision is to choose the confidence level associated with the loss cost analysis at step 404. For example if an underwriter chooses a 95% confidence level, the corresponding loss costs actually experienced should be less than this value 95% of the time. A unique characteristic of this invention is the capability of the underwriter to select a loss cost confidence level by year or, by default, use the same value for all years.

Another unique characteristic of this invention is the ability to apply different savings measurement criteria as claim triggers. One embodiment of the invention contains two types of savings measurement criteria although a combination or other methods could be applied.

The underwriter selects the measurement method and for this example of the invention, the methods are Escrow or No Escrow. The Escrow approach accumulates the excess above the risk acceptance values, if any, in the Savings over the policy years. If there is a shortfall in a policy year, the Escrow account may be debited first. A claim occurs when the Escrow account is zero and a yearly savings target is not achieved. The No Escrow method simple compares the actually achieved value, A, to insured Savings value, B, and a claim for the dollar difference $B-A occurs if A<B.

While the underwriter selects the measurement method in the system, it is not necessarily an input that is determined by the underwriting function. The claim measurement method may be identified as part of the policy and may be agreed to by the insured, insurer, and other interested parties such as investment firms, banks, or rating agencies (e.g., Standards & Poor).

At this point, FIGS. 5A and 5B illustrate the system for computing loss costs using a stochastic model that utilized the input annual aggregate risk distributions, risk acceptance values, the claims measurement method, and the required loss cost confidence level shown at steps 500 and 502. This is a dynamic system where at any one of these inputs change, the stochastic model is re-run at step 504. This combination of these policy-specific attributes and risk data to produce loss costs is a unique characteristic of this invention.

At the completion of the stochastic analysis which may require several thousands of different samples to accumulate the sufficient loss cost distributions, the loss costs at the underwriter specified levels is automatically placed into the pricing worksheet at step 506. The values are summed over the years of the policy term (e.g., over a range of one to seven years) at step 508 and compared with a company-specific requirement of a minimum rate-on-line at step 510. Rate-on-line is defined as the loss costs (or premium) divided by the total dollar exposure to the insurer. For example, a 5% rate-on-line requirement for a $1M total exposure produced a premium result of $50,000. The maximum of these two numbers: the sum of the loss cost values from the stochastic model and the rate-on-line estimated premium, is entered as the loss cost component of the multi-year policy premium at step 512.

With the loss costs determined, the underwriter adds premium charges that are due to the engineering and underwriting fees that will be required to administrate the policy over the policy term at step 514. These expenses include for example, on-site engineering review of work practices, initiative implementation progress, and the Savings measurement and verification procedures. These activities will generally vary according to the type of industry, facility location, policy term, policy conditions, and with several other factors. It is noted that the premium reflects the true costs of policy administration as well as the potential costs involved with actual losses. These costs are entered individually for each policy year, inflated using a supplied annual inflation rate, and summed to produce the overall engineering and underwriting (insurance) components at step 516. These costs are mostly well defined expenses and are not typically risk-based nor do they possess a significant stochastic component. At this point in the premium development, these charges are placed into the year and category (Underwriting or Engineering). Additional analysis of factors that influence the loss cost premium component is generally required before the expense items can be used further.

Along with the quantitative aspects of underwriting and premium development, there are subjective factors that are designed to utilize the underwriter's intuition and experience to modify, if desired, the computed loss cost premium at step 518. These factors can increase or decrease the loss cost component within prescribed percentage ranges. To facilitate the underwriter's use of these subjective factors, they are divided into engineering and underwriting categories. The actual list of risk modification factors and ranges will vary between industries and clients but they may include some of the items listed below.

A credit is interpreted as enhancing risk quality which then translates into a decrease the loss costs. A debit is configured as a decrease in risk quality which increases the loss cost component of the policy premium.

Engineering Quality Underwriting: [Debits, Credits]

1) Organization/Culture: [+15%, −10%]

Risk exposures, hazards, and human behaviors are inter-connected. A company's safety, environmental, reliability policies and basic cultural risk acceptance attitudes are important attributes for inferring how the corporation and its employees will routinely mitigate risk and also respond to accidents

2) New Technology Applications: [+10%, −10%]

Depending on the robustness of the new technology design, the operational and short term financial advantages can be offset by a decrease on reliability and availability in the long term. These factors need to be considered by the underwriter in this multiyear type of insurance policy which is intended to insure a minimum performance or Savings level.

3) Management Motivation: [+15%, −10%].

The underwriter needs to understand how the company's management intends to leverage the financial applications of the overall implementation and scheduling plan. The multi-year program will require the long term commitment of management and the financial applications of the program will provide the underwriter valuable insights to judge the Savings sustainability.

4) Supervisor Motivation: [+15%, −10%]

The underwriting risk assessment for facility supervisors may be similar to what may be required for management. At the employee-level, supervisors need to be committed to the implementation and scheduling plan's success and to its sustainability over the multi-year policy term. One way for the underwriter to assess supervisor (and management) commitment may be to determine how the execution of the Savings implementation and scheduling plan is connected to the employee bonus program.

5) Complexity: [+10%, −10%]

Complexity refers to the difficulty of program execution. Some of the issues to be considered in this evaluation are initiative technical difficulty, volumetric inter-dependence, and schedule inter-dependence.

6) Housekeeping and Recordkeeping: [+5, −5%]

The cleanliness, arrangement, and organization of the insured's assets are valuable, observable indicators to infer employee reliability and safety awareness. Many studies have shown a strong productivity and reliability correlations to facility and asset cleanliness and organization. This characteristic may be easy to observe and inference to improved reliability may be a factor in the engineering aspects of policy underwriting. Also the level and accuracy of production and operational recordkeeping may be another visible indication of employees' and management's commitment to procedure compliance and attention to detail that also reflects the engineering risk quality of the insured's facilities.

Overall the summation of the debits and credits of one embodiment is generally limited to a total of a 20% credit (premium decrease) or a 25% debit (premium increase.)

Underwriting quality refers to the terms and conditions of the insurance policy that are negotiated given the operational and engineering conditions of the implementation and scheduling plan. These risk modification factors measure risk quality from a written contractual, rather than technical, perspective.

The credit and debit assignments follow the same convention as with the engineering risk modification factors. A credit is interpreted as enhancing risk quality which then translates into a loss cost reduction. A debit is configured as a decrease in risk quality which is expressed as an increase the loss cost component of the policy premium.

Underwriting Quality Underwriting: [Debits, Credits]

1) Exclusions: [+10%, −10%]

These policy terms refer to events for which the insurance policy would not respond to Savings achievement levels below the insured minimum. These events include, war, worker strikes, weather events, events covered under other insurances, failure of the insured to comply with policy conditions, and contractor performance errors.

2) Self insured retention/Deductibles: (+10%, −10%]

The self insured retention or deductibles determine the insured's total financial risk exposure. If the insured is willing to assume higher annual Savings levels, then the risk quality from an underwriting perspective can be increased since the insured accepts a larger annual Savings shortfall before the insurance policy would respond.

3) Savings Measurement & Verification: [+15%, −10%]

The type of Savings and the procedures for measurement verification are fundamental to insurance underwriting. These factors are essential to determine initiative implementation quality both in time and volumetric savings achievement. There are, however, different ways these functions can be accomplished. For example, the measurement and verification can be performed by the insured and audited by the insurer, or a third party can be charged with these tasks. Involving the insured in these actions can be problematic and provide a moral hazard if insufficient oversight is not maintained. Savings measurement and verification can also provide a proactive indication of initiatives which are behind implementation targets. The underwriter needs to assess the type of measurements being taken to measure the Savings, the frequency of measurement, the ability to access this data trending, and the propensity to obfuscate actual initiative performance.

Overall the summation of the debits and credits are limited to a total of a 20% credit (premium decrease) or a 25% debit (premium increase.)

The aforementioned factors are routinely applied in policy underwriting and premium development depending on the type of insurance, life, casualty, property, etc. and also on the nature of the insured's business. The actual number and type of Engineering and Underwriting risk modification factors will vary depending on the type and nature of asset performance under policy consideration.

The “Adjusted Premium” is now computed at step 520. This term is defined as the aggregate policy term loss costs multiplied by the Engineering and Underwriting risk modification factors. If E=the aggregate engineering risk modification factor, U=the aggregate underwriting risk modification factor, and L the loss costs, then the adjusted premium, P_(adj) is determined by P _(adj)=(1+E+U)*L

The final stage of the premium development is to add premium components associated with insurance pricing elements at step 522. These items typically include engineering & administrative expenses, profit, reinsurance costs, taxes and commissions.

There are several variations and combinations of these factors that can be applied to the insurance product, rating system and method. The most notable variation may be the decision on how to account for the engineering expenses. Some insurance policies of the present invention may include all engineering fees in the policy premium and some may exclude the charges from the policy premium and charge these fees as consulting expenses independent of the insurance policy.

As an example of how the insurance policy pricing according to the present invention is performed, the following example shows premium development according to the present invention for a three year policy where engineering fees are incorporated into the premium calculation and is used to develop the loss costs.

An embodiment of the overall claimed subject matter follows in FIGS. 6A and 6B.

600 Input Basic Client Data into System.

At step 600, the user enters: Insured Name, Lending Institution, Country & Region, Addresses of Covered Locations, Occupancy, Location Size in Production Output Metrics, and Application of Insured Savings. This basic data can be integrated with a client database so that other key variables required by the system can be automatically identified from this basic data.

610 Develop the Numerical or Analytical Distributions of Savings by Year.

At this step an overall annual probability distributions are compiled and placed in a format so they can be accessed dynamically. The distributions describe the probability of exceeding annual Savings vs. the savings values. The distributions can be taken by analytical methods designed to compute aggregate Savings exceedance probabilities. There is a separate distribution for each location, plant, unit, or other segment under analysis for each year. These distributions are composed of Savings values and the corresponding probability of exceeding these values.

620 Enter Market and Company Pricing Criteria Data.

At this step, the inflation rate that is representative for the policy period and the minimum and maximum rate-on-line company-specified criteria are entered into the system.

630 Choose the Probability of Exceedance Thresholds to be Used to Set the Insured Floors: The Insured Savings Levels by Year, by Location, or by Other Groupings.

At this step the amount of risk that the insurer is willing to accept is determined by setting the exceedance probability threshold for coverage. There are two ways this can be done, the user can choose a probability of exceedance for all years or a different value for each year depending on the underwriting information. The probabilities are matched in the probability distributions compiled on step 610 and the corresponding Savings values are identified. For example, suppose the insurer is willing to accept an exceedance probability (measured in percentage) of 90% for a given location for a given year. This value is matched to the appropriate probability distribution discussed in step 610 and the corresponding Savings value is found to be $15M. This means there is a 90% chance that the location's annual savings that year will be greater than $15M. An insurance claim may be triggered if the annual savings achieved is less than the $15M value.

640 Record the Savings Levels by Year, Location, or Other Grouping in the Loss Cost Component of the Pricing Development System.

At this step, the resulting Savings values that are calculated or accessed from the probability distributions compiled in step 610, are entered into the loss cost component of the pricing system. These values are the resulting insured levels that correspond to the probability of exceedance values entered into the system in step 630.

650 Develop Logic to Test Annual Savings Results Selected from the Annual Probability Distributions Compiled in Step 610 to Measure Loss and Excess Event Frequency and Severity.

At this step for each year or other grouping, the logic is developed to compare a sampled distribution Savings value from the probability distributions compiled in step 610 to the recorded values savings floor Savings values. If the sampled Savings value is greater than the inured floor, then an excess is produced for that year. If the value is less than the insured level as given in step 640, then a loss event is produced for that year.

660 Develop Escrow Account and Claim Trigger Logic.

At this step, the comparison logic developed to accumulate the total or a fraction of the Savings results that are in excess of the insured savings values. For example, in one year if the computed Savings is $50 and the insured floor is $40, $10 would be credited to the escrow account. On the other hand, if the computed Savings was $35, then first the Escrow account would be debited $5 to obtain the insured level. If the escrow account contained insufficient funds then an insurance claim would be triggered for the difference between the insured level and the sum of the actual Savings results and any funds able to be drawn from the escrow account.

670 Develop Claim Count, Claim Amount and Claim Risk Distribution Logic.

At this step, logic is developed to accumulate the number and financial amount of claims for both the escrow and no escrow accounting methods. The financial amount of the claims is called the loss costs. This information is used to compute numerical distributions for the cumulative probability of loss as a function of the loss amount. These distributions are called claim risk distributions.

680 Run Stochastic Model to Develop Claim Risk Distributions.

At the step, a numerical procedure is applied using commercial software or specialized programming that applies steps 640, 650, and 660 to accumulate sufficient loss data to develop a numerical distribution of the probability of loss as a function of the loss amount for both the escrow and no escrow accounting approaches.

690 Determine Rate-on-Line Premium.

At this step, the prescribed rate-on-line criteria selected in step 620 is applied to each annual exceedance threshold selected in step 640. The rate-on-line premium calculation may be performed by multiplying the exceedance threshold, the insured Savings minimum, or floor by the decimal value of the rate-on-line. For example, if the insured floor is $10,000 and the rate-on-line is 10%, then the premium requirement is $10,000*0.10 or $1,000. These calculations are applied to each insured annual savings floor as computed in step 640. The results are summed and placed in a Term of Loss Cost Summary Section of the system.

700 Determine Loss Cost Confidence Level and Loss Cost Values.

At this step the underwriter enters the likelihood requirement, in percent, that the loss costs obtained from the system will be actually less than the identified values. These percentages are then applied to the claim risk cumulative distributions for each year to determine corresponding value for the yearly loss costs contribution to the total multi-year premium. The resultant values are placed in the yearly loss cost fields. This is performed for the claim risk distributions with and without escrow accounting.

710 Compute Loss Cost Policy Premium Component.

At this step, the rate-on-line premium values for each year are summed to compute the total policy premium via the rate-on-line method. Next, the annual loss costs determined in step 680 are summed over the policy years for the Escrow and No Escrow pricing methods. The system user then selects which Escrow pricing method may be required for the client. The system subsequently computes the policy loss cost premium component as the maximum of (1) prescribed rate-on-line, and (2) the summed loss costs via the Escrow method selected.

720 Determine Underwriting Expenses.

At this step, the company expenses, required to perform the underwriting analysis and risk surveillance are entered. These costs are incurred in reviewing monthly, quarterly, and yearly Savings reports and periodically meeting with client management at the client sites. The underwriters' responsibility is to ensure the client is meeting their contractual responsibilities and the Savings targets. If the client is in compliance then coverage continues as defined in the policy. If the client is not in compliance, then it is the underwriters' responsibility to notify company engineering and notify client management, in writing. If compliance with engineering recommendations and other policy conditions are not met in the time constraints as specified in the policy, then the underwriters have the responsibility and the authority to terminate insurance coverage. The expenses incurred performing these activities are entered into the system for each policy year.

730 Determine Engineering Expenses.

At this step the technical engineering, project management, and Savings oversight activities are reviewed for compiling their associated policy expense costs. Engineering activities provides technical data to support underwriting activities, provides periodic loss prevention and Savings reporting, provides technical directions for initiative implementation, and serves as the on-site liaison between the insurer and the insured. The expenses incurred performing these activities are entered into the system for each policy year.

740 Determine Engineering Related Underwriting Credits and Debits.

At this step, pricing modification factors are determined that increase or decrease the premium based on engineering related attributes of the Savings implementation insured values as selected in step 620. These factors include, but are not limited to, the insured's organization and business culture, new technology applications, management motivation to achieve the Savings targets, supervisor motivation, and plant complexity. The range of the modifiers will vary with application but generally are 10% for each factor with an aggregate factor of no less than −20% and no greater than +25%. The engineering risk modification factors are entered into the system for each policy year and an aggregate modification factor is computed.

750 Determine Underwriting Related Credits and Debits.

At this step, the pricing modification factors are determined that increase or decrease the premium based on the underwriting related attributes of the Savings insured values as selected in step 620. These risk modification factors include, but are not limited to, policy exclusions that are in place, the insured self insured retention, deductibles, limits, and the Savings measurement and verification program quality. The range of the modifiers will vary with application but generally are 10% for each factor with an aggregate factor of no less than −20% and no greater than +25%. The underwriting premium modification factors are entered into the system for each policy year and an aggregate modification factor is computed.

760 Compute Adjusted Policy Premium.

At this step, the numerical results determined in previous steps are combined to produce the basic policy premium. There are several versions or combinations of the steps outlined in this procedure that are claims. An example of one such embodiment is:

Adjusted Policy Premium=Step 710 (Loss Cost Policy Premium)*[1+Step 740 Engineering Modification Factors)+Step 750 Underwriting Modification Factors)]. This result is stored in the Premium: Insurance Adjusted Premium Section of the system.

770 Compute Policy Underwriting and Engineering Expenses.

At this step the underwriting expenses determined in step 720 and the engineering expenses determined in step 730 are inflated using the inflation rate entered into the system in step 620 over the policy term and summed to compute the total policy level underwriting and engineering expenses. These results are stored in the Premium: Insurance and Engineering Expense Sections of the system.

780 Compute Engineering and Underwriting Profit.

At this step, company-specific guidelines are applied to compute insurance and engineering profit based on the expenses computed in steps 760 and 770. These results are stored in the Premium: Profit-Insurance and Engineering Sections.

790 Compute Allocated Reinsurance Costs.

At this step, reinsurance costs, whether facultative or treaty related, are entered into the Reinsurance section of the system.

800 Compute Taxes.

At this step, taxes are computed on the pertinent sections of the Premium Section of the system and entered in the system in the Premium-Insurance and Premium and Engineering: Taxes Section.

810 Compute Commissions.

At this step insurance related commissions are computed on the pertinent sections of the Premium Section of the system and entered in the system in the Premium-Insurance: Commissions Section.

820 Compute Total Policy Engineering Costs.

At this step, all premium costs entered into the Premium-Engineering related sections are summed to compute the total policy engineering costs.

830 Compute Total Policy Premium.

At this step, all premium costs entered into the Premium-Insurance related sections are summed to compute the total policy premium. Also, based on the policy requirements and the pertinent accounting procedures, the total policy premium can also include the total engineering costs. In this scenario, all risk transfer and direct engineering costs required to support the policy are included in the total policy premium which is divided by the policy term to determine the annual premium. Depending on the insurance conditions, the insured may pay the whole premium at the beginning of the policy term or pay on an annualized basis.

FIGS. 7A and 7B depicts a spreadsheet encompassing the steps disclosed in FIG. 6.

The methods disclosed above can be used to ascertain a securitization rating VB and FS ratings can be based on benchmark data for a particular asset, e.g. the power generation station of FIG. 1.

For example, FIG. 8 illustrates such method. Engineering data such as improving yields 940 or other initiatives 950, both depicted in FIG. 8A is collected for each required asset at step 900. The engineering data is compared with benchmark data to create an action plan and financial goals at steps 910 and 920. FIG. 8B illustrates an exemplary action plan while FIG. 8C illustrates the financial goals.

For example, FIG. 8B includes various actions to be initiated by employees 960, such as detailed process evaluation 970 and train operators 980. FIG. 8C shows how the risk curves can be used to select annual insurance levels and also provide information to select financial goals for the improvement program overall. For example, following general insurance company guidelines, a company chooses the 90% exceedance probability and moves horizontally over until we cross the Year #1 risk curve at 990 where at 90% risk acceptance value for insurance purposes is $20M at 995. This typically means there is a 90% chance that the actual result will be greater than $20M. The company can also use these risk curves to set their internal financial goals at more aggressive risk acceptance values. For example, company management may target the 60 or 70% levels for the business unit targets which for year #1 would be a goal between approximately $22-$25M. The same procedure is applied for Year #2. The insurance risk acceptance percentile intersects the risk acceptance curve at 1000 which corresponds to $26.7M NCM annual savings at 1005. This amount would be selected as the insured floor. For the company's internal financial goals, using the 60-70% guidelines as in Year #1, Year #2 company financial goals would be between $28-29$M. A securitization rating can be ascertained based on the action plan and financial goals at step 930 (FIG. 8).

Implementation of the present invention may also improve an insured's bond rating. FIG. 9 illustrates cost savings as a result of a reduction of credit risk. For example, suppose improving the operations utilizing the present method can increase the Savings by $700 million of an insured over a ten (10) year period. In the course of developing this company's credit risk for the purpose of developing a bond issue, the lending institution and or credit agency involved may give the company credit for the enhanced operational and financial status by applying the margin benefit to the reduction of the principal at risk. This may be a subjective decision. However, the method applied to this situation offers a risk transfer of principal from the client to the insurer thereby securitizing at least a portion of the principal. Suppose the client has a credit rating of BB− by S & P. A policy utilizing the present invention for this client can have effect to reduce the principal at risk thereby also reducing the transaction's credit risk. Through the risk transfer of this principal to the insurance company, the initial transaction (now at effectively a lower principal) can have an equivalent credit risk of the full bond amount at a higher quality credit rating.

For example, if a client has an $600M policy according to the present invention for over the ten (10) years of a $800M bond, the reduced effective principal at risk ($200) make the transaction appears, from a credit risk perspective as slightly above investment grade, BBB−. This means mathematically the credit risk of a $200 BB− bond may be roughly equivalent to the credit risk of an $800M bond rated at BBB−. This situation illustrated at 1010 in FIG. 9. This example assumes the insurance company's credit rating is at least BBB−.

Referring to FIG. 10, a computer system used to implement some or all of the method and system is illustrated. The computer system consists of a microprocessor-based system 1100 that contains system memory 1110 to perform the numerical computations. Video and storage controllers 1120 enable the operation of the display 1130, floppy disk units 1140, internal/external disk drives 1150, internal CD/DVDs 1160, tape units 1170, and other types of electronic storage media 1180. These storage media 1180 are used to enter the risk distributions to the system, store the numerical risk results, store the calculation reports, and store the system-produced pricing worksheets. The risk distributions can be entered in spreadsheet formats using, e.g., Microsoft Excel. The risk calculations are generally performed using stochastic methodologies such as Monte Carlo simulations either by custom-made programs designed for company-specific system implementations or using commercially available software that is compatible with Excel. The system can also interface with proprietary external storage media 1210 to link with other insurance databases to automatically enter specified fields to the pricing worksheet, such as client name, location address, location size, location occupancy, and risk quality attributes applied in the “Credits and Debits” section. The output devices include telecommunication devices 1190 (e.g., a modem) to transmit pricing worksheets and other system produced reports via an intranet or the Internet to management or other underwriting personnel, printers 1200, and electronic storage media similar to those mentioned as input device 1180 which can be used to store pricing results on proprietary insurance databases or other files and formats.

FIG. 11 is a block diagram that depicts the terms and conditions of an insurance policy 1300 according to the present invention. The insurance policy 1300 includes insured information 1310 such as a name of the insured, geographic or physical location(s) of the insured to be covered by the policy. Also included in the policy 1300 is a policy period 1320. The policy period 1320 can be over a single year, multi-year or some other defined period of time. Policy terms 1330, such as savings criteria is included. The savings criteria are generally crafted by a third party company (e.g., HSB Solomon Associates) that uses benchmark information in creating the savings criteria based on the particulars of the insured's business. The savings criteria include processes that if implemented by the insured establishes a sum certain savings to the insured. The third party company can serve as a facilitator in process execution enabling the insured to improve operating performance (resulting in a savings). If the process is implemented and the sum certain savings is not realized by the insured over the policy term (with certain exceptions outlined in the policy), the insurer will pay the insured the difference (referred to as a shortfall). The certain exceptions include, but are not limited to, hostile or warlike action, insurrection, rebellion, civil war, nuclear reaction or radiation, default or insolvency of the insured, vandalism, riot, failure of contractors to implement the processes, modification or alteration to the processes that were not approved by the insurer or other terms outlined in the policy. Other policy terms 1330 include duties of the insurer and duties of the insured such as execution of the processes in a timely manner, cooperation with the third party company, preparation of status reports, permission by others to audit the insured's accounts, performance records and data logs and other matters. Furthermore, if the savings are determined on a yearly basis and the policy is a multi-year policy, and as a result of the insured implantation of the processes, a shortfall occurs, such shortfall could be kept in an escrow account (herein referred to as a surplus account). The escrow could increase or decrease over the multi-year policy. Any surplus at the end of the policy term can be paid to the insured. Other terms can include cancellation terms, representations and warranty, assignment obligations and effects due to the sale or transfer of a covered location.

The policy 1300 also includes monetary policy limits (i.e. limit of liability) 1340 over the time period 1320 and premiums 1350 to be paid by the insured and endorsements 1360. Such endorsements can include market price indexing and operational baselines unique to the insured's industry, the implementation plan and schedule, agreed metric plan, savings calculation procedures and baseline values, debt obligations and additional exclusions, definitions and conditions.

FIGS. 12A-12D illustrate an agreed metric plan. The agreed metric plan provides top level task lists of an implementation plan and schedule. For illustrative purposes, the plan is divided into four sections, namely, initiative 1400, benefits and measurements 1402, implementation 1404, and savings 1406.

For example, in FIGS. 12A, 12B, 12C and 12D, the agreed metric plan pertains to a chemical industry policy. From the implementation plan and schedule, various top level initiatives 1400 are listed in FIG. 12A. For example, some of the initiatives from a chemical industry policy may include movement of an analyzer to trays and modification of regulatory controls on final product columns 1408 for a particular plant 1410 (Initiative #1). The column titled “area” refers to geographical or functional location the initiative, e.g., Plant 1. Another initiative for an insured's site may include the reduction of pressure in a stripper to save energy 1412 (Initiative #2). Furthermore, another initiative for another plant may include the reduction of time to dry catalyst after regeneration 1414 (Initiative #3). Documents or other deliverables 1418 are provided to document the results of the implantation of the initiatives. An example of such a document 1420 may include a report describing the savings achievement as a result of the implantation of initiative #3.

One embodiment of the benefits and measurement section of the agreed metric plan is provided in FIG. 12B. Implementation of the initiative may result in certain benefits that are described in this section. For example, for initiative #1, one benefit 1422 may be a production efficiency improvement. The plan includes various measurement values and methodologies that are directed to the results of the initiative. The values and methodologies may relate to engineering units (e.g., t/h) and time periods (e.g., measurements are done daily and then averaged over a period). Furthermore, the plan includes dates (target and actual) for the commencement of the initiatives and completion dates of the initiatives. The agreed metric plan typically requires the agreement and sign off (e.g., initials of the insured and insurer) 1424 of each initiative and initiative results (i.e., the agreement section).

The plan also includes target and actual dates as shown in FIG. 12C. Each initiative may have a target date of completion 1426, actual date of completion 1428 and the number of days for completion 1430.

The plan also include information regarding the economic Savings 1432 as a result of the implantation of the initiatives. Such information may include target Savings 1434 and actual Savings 1436 achieved as a result of the initiative.

As discussed earlier, various embodiments disclosed herein involve a credit enhancement system and method and generally relate to the manner in which insuring a project savings floor (i.e., a minimum level of return on investment for a given improvement project) can be realized in a better credit rating for bonds or loans associated with the improvement project. See, e.g., FIGS. 8A-8D, and FIG. 9. A better understanding of such embodiments can be obtained in light of the below detailed description of various preferred embodiment and FIGS. 13-11.

FIG. 13 is a flowchart of an embodiment of the insurance product, rating system and method for insuring project savings to enhance insured's credit rating, which generally relates to the manner in which insuring a project savings floor can be realized in a better credit rating for bonds or loans associated with the improvement project. This embodiment comprises a credit enhancement system and method based on an insurance product that is designed for a relatively small number of insureds annually or over a multi-year term with each insured having a relatively large exposure. This situation cannot rely on the Law of Large Numbers principle of statistics but applies as much knowledge and actual performance data as possible into the development of the risk analysis. Therefore each situation is treated individually.

Referring to FIG. 13, in 1500, the user computes an insured floor of savings for a given project. In this embodiment, the insured floor of saving (i.e., the minimum return on investment) is computed using the various embodiments of the system and method of U.S. Pat. No. 8,195,484. At 1510 the project aggregate risk distribution is recomputed to reflect the insured floor decision of the user. In 1520 the credit risk calculations are computed using the insured floor modified project savings distribution and in 1530 the influence of the modified project savings stream is measured in terms of reduction in credit risk due to the insured floor project savings stream

The steps of embodiment illustrated in FIG. 13 are further explained in more detail. FIG. 14 illustrates a flowchart for determining an insured savings floor for a project. In 1600, the user develops a project improvement plan which contains details about each initiative which in aggregate are designed to produce the planned level of savings. Related to this initiative listing and description is the initiative implementation schedule 1610 which describes the expected start date, and implementation duration time. To quantify the uncertainty in the execution of each initiative, detailed benchmarking data 1620 is applied to provide the range of possible outcomes for each project initiative both in performance and schedule. In 1630, the initiatives are compiled into a file that computes the aggregate project savings distribution. In 1640, the underwrite inputs the desired confidence level at which the insured savings floor is desired. As used herein, “confidence level” is the probability that the annual savings will exceed the insured floor value. A Monte Carlo analysis is then performed in 1650 which utilizes the confidence level entered at step 1640 and the aggregate initiative risk listing in 1630 to produce the aggregate project savings risk distribution. In 1660, the computed aggregate project savings risk distribution is used along with the user specified confidence level to determine the actual value of the insured floor.

FIG. 15 illustrates an example of how insured savings floor is determined. In this example the user specifies a 90% confidence level 1700. This means that there is a 90% chance that the actual project savings will exceed this value. From the desired confidence level (e.g. 90%) on the vertical axis 1700, the user goes vertically across the graph until the line intersects the distribution curve 1710. The risk acceptance value is the corresponding value on the horizontal axis 1720. Performing this functional analysis is called “risk acceptance.” In this example, the user entered a confidence level of 90% and reading the corresponding value off the x axis shows an insured floor of $96,000 per year 1720. The cumulative probability curve shown in this format signifies that there is a 90% chance that the project savings will exceed $96,000 per year. Or stated differently, this format signifies that there is a 10% chance that the annual savings will be below this value. This is the risk that the insurer accepts at the 90% confidence level in this case.

FIG. 16 illustrates an example of how insurance modifies the computed aggregate project savings risk distribution. Notice that the project savings cannot be lower than $96,000 from the insured's perspective. In the instances where the actual project savings did fall below the floor, the insurer would pay the difference. This is the purpose of the risk transfer process of insurance.

In FIG. 17, the integration of the insured floor aggregate project savings risk distribution with the project credit risk calculations is illustrated. In 1800 the credit risk calculations require the user to enter the loan interest rates by rating category. In one embodiment, these values are from a commercial rating agency like Standard & Poor's or from other sources and the insured floor aggregate project savings risk distribution is included as a data input 1810. The credit risk model also requires data regarding the annual or periodic default probabilities for the time intervals being considered in the analysis. These values are entered in 1820. The specific loan characteristics: loan amount, term, and payment schedule are entered in 1830.

With this information now compiled, the net payment amounts are computed for each rating category in 1840. The term, “net,” is used here to refer to the standard loan payment adjusted for the inclusion of various amounts of the project savings. Since the project savings is inherently in stochastic nature, various statistical representations of savings can be applied. In the preferred embodiment, the percentile statistics of the project savings are applied. Other statistics or values could be used such as an average savings of a user entered value. The net periodic loan payment incorporating different levels of project savings using the insured floor distribution is given by deducting the percentile aggregate savings statistics of 1810 from the basic loan payment amount.

In 1850 the credit risk model computes the default amounts for each rating category and for each percentile modified loan payment amount. In one embodiment, a Monte Carlo analysis is applied in 1860 that computes the average default loss amounts for each loan payment percentile situation for each rating category. In 1870, these values are stored in a table for further analysis by the next step in the invention.

An example of the graphical representation of the information placed in tabular form is illustrated in FIG. 18. In this example, the loan amount was $2,500,000 repayable with 10 annual payments for customers at S & P credit ratings from B− to A. The zero percentile (basic loan payment) and the 50, 80 and 100 percentile project savings values are illustrated. These values create the separate lines.

This framework can be applied to demonstrate graphically how the net project savings distributions can modify credit risk valuations using FIG. 18. For an example, consider a B− company where the 100^(th) percentile savings value is applied to model the net term payment. This exercise demonstrates the maximum credit risk reduction possible from the project savings in this transaction.

At 1900, the credit risk is shown to be reduced from about $1,000,000 to roughly $300,000. At the reduced value at 1910, move horizontally across the plot until intersecting the base loan (0^(th) percentile) credit risk curve. In 1920 move down to the rating axis and note the credit rating improvement.

This exercise shows that in the illustrated example, a B− rated loan with the 100 percentile of the project savings applied to the loan payment has approximately the same credit risk as a BB rated company with the standard loan. Thus from a credit risk perspective a B− rated company with the assumed project savings applied to the loan payment is equivalent to a BB rated standard loan. This procedure demonstrates the process of determining the credit risk equivalences using project savings to offset some of the standard loan payment amount.

FIG. 19 illustrates an example of how this process is applied to the default amounts listed in the table generated in 1870. To analyze possible credit enhancement for a B− company in 2000 in the foregoing example, the base credit is noted for the next higher level, B. Then this default risk amount is located or bracketed in the B− column. The associated values in the project savings percentile column indicates how much of the project savings stream need to be attributed to the loan payment to obtain the equivalence in credit risk to a standard B− loan. The 2000 amount for B rated equivalency shows that between only 0 and the 10^(th) project savings percentile amount is required, in 2010 a B+ rating is possible in the 30^(th)-40^(th) project savings percentile level, in 2020, a BB− rating is achieved at the 70^(th)-80^(th) project savings percentile level, and in 2030, a BB rating is achieved at the 90^(th)-100^(th) project savings percentile level. Then note that the base credit risk value for a credit risk rating of BB+ is not included in even assuming the 100^(th) project savings level. This fact defines the limit credit enhancement benefits can be achieved from this associated project for an initially rated B− company.

This financial information enables analysts to assess the credit enhancement confidence they can expect from the project savings stream. In this application of the percentile results, the lower the value applied to achieve a desired credit enhancement, the higher will be the confidence that the project will deliver the desired credit enhancement results.

To summarize this quantitative information regarding the amount of credit enhancement as a function of the project savings distribution percentiles, the procedure discussed in FIG. 19 can be applied to the credit risk table compiled in 1870 of FIG. 17. This result is an output of various embodiments disclosed herein, and represents important risk- or probability-based information on credit enhancement that rating analysts, project financiers, and insurance underwriters can apply in their work.

An example of these results is shown on FIG. 20A. As illustrated, the probability of exceeding the project savings level required for the credit enhancement is given for each possible rating category. The arrow represents an important transition from the non-investment grade level, BB+, to investment grade, BBB−. This is a critical transition and can have benefits beyond the straightforward credit risk calculations shown here.

The arrow demonstrates that for a BB+ rated company used in this example who is considering a $2.5M loan repaid in ten years, there is a 92% chance that the actual project savings will be greater than what is required to achieve an equivalent credit risk transaction of a BBB− rated company. FIG. 20B shows the corresponding reduction in credit risk which will be savings if the transaction actually achieves the BBB− rating designation. In FIG. 20C, the value of the project savings is given that achieves the corresponding credit rating enhancement. For the BB+ to BBB− transition, this annual amount is shown to be $64,233. But from FIG. 20A, there is a 92% chance that the achieved project savings will be greater than this amount with a corresponding overall loan reduction in credit risk, shown in FIG. 20B of $55,956.

This information from the illustrative embodiments provides all interested partners the risk-based information required to make project management, investment, and rating decisions. In addition, this information can assist financial analysts analyze project savings risk as it specifically applies to debt repayment. This feature represents a novel aspect of this of the disclosed embodiments.

Some embodiment can be used to demonstrate the value created by insuring a minimum savings floor. In one embodiment, the insured floor is selected to apply to the aggregate project savings expected over its six year duration instead of annually as shown in the preceding example. The project activities are expected to produce over six years between $450 and $750 million dollars of savings for the company. In order to accomplish the required activities to achieve these savings, the company needs to borrow $750M for a payback over the six years of the project duration with fixed annual payments.

For this loan amount and duration the Table 1 illustrates typical loan repayment amounts.

TABLE 1 Example Interest Rates and Annual Loan repayment Amounts by S & P Credit Rating Category S & P Rating Annual Annual Loan Re- Category Interest Rate payment Amount B− 10.345 $173,950,209 B 10.000 $172,205,535 B+ 9.500 $169,689,962 BB− 9.345 $168,913,264 BB 9.000 $167,189,837 BB+ 8.735 $165,871,087 BBB− 7.565 $160,101,637 BBB 6.675 $155,771,597 BBB+ 5.785 $151,493,203 A− 5.530 $150,277,008 A 5.275 $149,065,142

To apply the project savings to debt payment, the overall project level aggregate savings risk distribution is presented in FIG. 21. This plot is the result of risk modeling developed from the exemplary project's initiative and benefit descriptions, operational baselines and market price indexing, savings execution plan schedule, agreed metric, and savings measurement and loss adjustment plans. Applying a 90% confidence level in 2100, the corresponding risk acceptance value is shown in 2110 as about $570M. According to FIG. 21, there is a 90% chance that the actually achieved project savings will exceed this amount. The insurance underwriter chooses this amount, $570M, as the insured floor.

The important question addressed by this embodiment of the invention is how the insured floor influences the credit worthiness of the loan. The process to measure this effect is to execute the system and method steps as shown in FIGS. 17 and 19, first with and without the insured floor, and then to compare the results in the format of FIGS. 20A and 20B.

FIGS. 22A and 22B show, for the exemplary project, the possible credit risk equivalences and the corresponding likelihood that the required project savings will be achieved by credit rating category. For any rated company, only one row would be relevant. Still, the entire table shown in FIG. 22 presents financial underwriters and investors with a broad range of credit enhancement possibilities from the quantitative results of the disclosed embodiments.

Two illustrations are discussed to demonstrate how the illustrated embodiment provides risk-based information for decision-making. In FIG. 22A, 2200, the no insurance floor case shows that B− rated company can achieve a B and B+ ratings by applying less than 1% of the anticipated savings. In other words, there is a 100% chance that the actual savings achieved will be greater than the amount required to achieve this stated credit risk equivalence. Yet this is the limit that can be achieved in this case. There is only a 4% chance that the project savings level required for this credit improvement will be achieved. It is highly unlikely that an analyst would agree to credit rating improvement given the low probability of success.

In FIG. 22B in 2200, the application of the insured floor shows that there is a 100% chance that the project savings level for the B− to BB− improvement will be achieved. This improvement in likelihood for achieving the project savings level required for a given credit rating enhancement provides financial analysts with the critical, risk-based information for credit risk decision-making.

Referring to 2210 in FIGS. 22A and 22B, an important application of the disclosed embodiments is described. Credit rating enhancement analysis is always important but it takes on strategic value when a credit enhancement can take a non-investment grade transaction, i.e., from an investment grade less than a BBB− rating to a BBB− rating or higher. In 2210, the non-investment grade transaction of a BB− has only a 4% likelihood of achieving the required project savings. However the insured floor likelihood of achieving the required project savings is 100%, thus making the BB− to BBB rating improvement more likely by analysts in light of the insured floor.

FIGS. 22A & 22B refer to the risk-based details of credit improvement but the value of the associated credit risk reduction is shown in FIGS. 23A & 23B. In 2300 the credit risk reduction from transitioning from a B− to BB− is $152,413,179 and in FIG. 23B where the insured floor is in place, the average credit risk savings in shown as $153,287,348. These values are statistically the same indicating that the credit risk savings are independent of the insurance. A similar exercise in 2310 in FIGS. 23A and 23B shows the same result. The invention quantifies the result that while the insurance can help the analysis and decision-making associated with credit enhancement, the actual credit savings, given the transition is made, is independent of the insured floor.

FIG. 24 broadly illustrates an embodiment of the insurance product, rating and credit enhancement system for insuring project savings 2410 comprising an input device 2412, a processor 2414, an output device 2416, and a storage subsystem 2418. In one embodiment, the input device 2412 is a keyboard used by the user to enter input files. In other embodiments, the input device comprises a medium whereon digital input files are stored, such as, for example, a portable hard drive. In yet other embodiment, the input device 2412 comprises a telecommunications device, such as a computer modem, that facilitates the receiving of input files from a wired or a wireless network. As discussed further herein, the input files include annual aggregate risk distributions, which quantify the net performance risk of all the initiatives for achieving the net annual savings for each year of the policy period. For example, the input files for an improvement program may consist of information on work force reassignments, process re-designs, installation of advanced process controls, and energy efficiency capital projects. The storage subsystem 2418 can be used to store the input files and the digital information that facilitates the various functionalities of embodiments disclosed herein. The processor 2414, interacting with the storage system 2418, accesses and executes the instructions that comprise the described aspects and functionalities of the embodiments disclosed herein. The output device 2416 can be any computer display device such as a computer monitor or a printer.

The foregoing disclosure and description of the invention are illustrative and explanatory thereof, and various changes in the details of the illustrated method may be made without departing from the spirit of the invention. 

We claim:
 1. A computer implemented method, comprising: receiving, by a specifically programmed computer, a plurality of loan interest rates by rating category, and default probability data for a plurality of time intervals; receiving, by the specifically programmed computer, a loan amount, a loan term, and a payment schedule; receiving, by the specifically programmed computer, insured floor project savings risk distribution data for an improvement plan for a facility; for each rating category, computing, by the specifically programmed computer, net periodic loan payments for a plurality of confidence levels of project savings based on the insured floor project savings risk distribution data; for each rating category, computing, by the specifically programmed computer, default amounts for the plurality of confidence levels of project savings, based on the net periodic loan payments for such rating category; determining, by the specifically programmed computer, equivalent credit risk relationships based on the default amounts for the plurality of confidence levels of project savings at each rating category; computing, by the specifically programmed computer, a project loan credit rating enhancement for the improvement plan for the facility based on the equivalent credit risk relationships; and analyzing, by the specifically programmed computer, a pricing of an insurance policy based on the project loan credit rating enhancement associated with implementing the improvement plan for the facility.
 2. The method of claim 1, wherein the facility is a power generation plant.
 3. The method of claim 1, wherein the facility is a chemical plant.
 4. The method of claim 1, wherein the facility is a refining plant.
 5. The method of claim 1, wherein the facility is a manufacturing plant.
 6. A computerized system, comprising: at least one programmed computer, having a non-transient computer tangible readable medium having stored thereon software instructions executable by at least one processor of the computer, wherein the software instructions comprise: code to receive at least the following: i) a plurality of loan interest rates by rating category, ii) default probability data for a plurality of time intervals, iii) a loan amount, iv) a loan term, v) a payment schedule, and vi) insured floor project savings risk distribution data for an improvement plan for a facility; code to, for each rating category, compute net periodic loan payments for a plurality of confidence levels of project savings based on the insured floor project savings risk distribution data; code to, for each rating category, compute default amounts for the plurality of confidence levels of project savings, based on the net periodic loan payments for such rating category; code to determine equivalent credit risk relationships based on the default amounts for the plurality of confidence levels of project savings at each rating category; code to compute a project loan credit rating enhancement for the improvement plan for the facility based on the equivalent credit risk relationships; and code to analyze a pricing of an insurance policy based on the project loan credit rating enhancement associated with implementing the improvement plan for the facility.
 7. The system of claim 6, wherein the facility is a power generation plant.
 8. The system of claim 6, wherein the facility is a chemical plant.
 9. The system of claim 6, wherein the facility is a refining plant.
 10. The system of claim 6, wherein the facility is a manufacturing plant.
 11. The method of claim 1, wherein the method further comprises: computing, by a specifically programmed computer, project improvement distribution data for the improvement plan for the facility; computing, by the specifically programmed computer, an insured floor of savings for the improvement plan for the facility; and computing, by the specifically programmed computer, the insured floor project savings risk distribution data for the improvement plan for the facility based on: i) the insured floor of savings for the improvement plan for the facility and ii) the project improvement distribution data for the improvement plan for the facility.
 12. The system of claim 6, wherein the software instructions further comprise: code to compute project improvement distribution data for the improvement plan for the facility; code to compute an insured floor of savings for the improvement plan for the facility; and code to compute the insured floor project savings risk distribution data for the improvement plan for the facility based on: i) the insured floor of savings for the improvement plan for the facility and ii) the project improvement distribution data for the improvement plan for the facility. 